Search Results (27)

The non-uniform production contribution caused by insufficient reservoir stimulation during initial fracturing significantly constrains the lifecycle and estimated ultimate recovery (EUR) of horizontal wells. Refracturing is therefore urgently required to reconstruct fracture networks and activate undeveloped reserves. In this study, a coupled geomechanics-matrix-fracture-seepage model is developed based on the Unconventional Fracturing Model (UFM) to characterize formation energy evolution and residual oil distribution. Simulation results indicate that initial fracturing creates a limited pressure diffusion radius (5–30 m), resulting in a “strong near-well, weak far-field” pressure distribution and inefficient residual oil utilization. To address this, a synergistic strategy is proposed, integrating “re-pressurization of existing fractures” for energy replenishment with “infill fracturing” for activating bypassed reserves. This strategy significantly outperforms conventional refracturing, increasing the predicted cumulative oil production by 55.86%. Parameter optimization indicates that maintaining a pumping rate of 10–12 m³/min and a fluid intensity of 1700–1900 m³/stage, while optimizing proppant ratios for conductivity, maximizes recovery. This work provides theoretical guidance for sweet spot evaluation and refracturing design in ultra-low permeability reservoirs. Full article

This paper provides research that shows that the scale and quantification of the degree of fracturing in a rock mass should and can be considered when estimating geological strength index (GSI) ratings for rock mass strength and deformability estimates. In support of this notion, a brief review is provided to demonstrate why it is imperative that scale is considered when using GSI in engineering design. The impact of scale and scale effects on the engineering response of a rock mass typically requires a definition of fracture intensity relative to the volume or size of rock mass under consideration and the relative scale of the project being built. In this research three volume scales are considered: the volume of a structural domain, a representative elemental REV, and unit volume. A theoretical framework is established that links these three volume scales together, how they are estimated, and how they relate to parameters used to estimate engineering behaviour. Analysis of data from several examples and case histories for real rock masses is presented that compares and validates the use of a new and innovative but practical method (a sphere of unit volume) to estimate fracture intensity parameters VFC or P30 (fractures/m³) and P32 (fracture area—m²/m³) that is included on the vertical axis of the volumetric V-GSI chart. The research demonstrates that the unit volume approach to calculating VFC and P32 used in the V-GSI system compares well with other methods of estimating these two parameters (e.g., discrete fracture network (DFN) modelling). The research also demonstrates the reliability of the VFC-correlated rating scale included on the vertical axis of the V-GSI chart for use in estimating first-order strength and deformability estimates for rock masses. This quantification does not negate or detract from geological logic implicit in the original graphical GSI chart. Full article

Friction stir welding (FSW) is a solid-state joining technique widely used for aluminum alloys in aerospace, automotive, and shipbuilding applications, yet the prediction of fatigue fracture locations within FSW joints remains challenging for structural-life assessment. In this study, we investigate fatigue fracture location prediction in 7075-T651 aluminum alloy FSW joints by applying four machine learning methods—decision tree, logistic regression, a three-layer back-propagation artificial neural network (BP ANN), and a novel Quadratic Classification Neural Network (QCNN)—using maximum stress, stress amplitude, and stress ratio as input features. Evaluated on an experimental test set of eight loading conditions, the QCNN achieved the highest accuracy of 87.5%, outperforming BP ANN (75%), logistic regression (50%), and decision tree (37.5%). Building on QCNN outputs and incorporating relevant material property parameters, we derive a Regional Fracture Prediction Formula (RFPF) based on a Fourier-series quadratic expansion, enabling the rapid estimation of fracture zones under varying loads. These results demonstrate the QCNN’s superior predictive capability and the practical utility of the RFPF framework for the fatigue failure analysis and service-life assessment of FSW structures. Full article

In the field of oil and gas well perforation, it is imperative to accurately forecast the casing entry hole diameter under full downhole conditions. Precise prediction of the casing entry hole diameter enhances the design of both conventional and limited entry hydraulic fracturing, mitigates the risk of proppant screenout, reduces skin factors attributable to perforation, guarantees the presence of sufficient flow areas for the effective pumping of cement during a squeeze operation, and reduces issues related to sand production. Implementing machine learning and deep learning models yields immediate and precise estimations of entry hole diameter, thereby facilitating the attainment of these objectives. The principal aim of this research is to develop sophisticated machine learning-based models proficient in predicting entry hole diameter under full downhole conditions. Ten machine learning and deep learning models have been developed utilizing readily available parameters routinely gathered during perforation operations, including perforation depth, rock density, shot phasing, shot density, fracture gradient, reservoir unconfined compressive strength, casing elastic limit, casing nominal weight, casing outer diameter, and gun diameter as input variables. These models are trained by utilizing actual casing entry hole diameter data acquired from deployed downhole cameras, which serve as the output for the X’ models. A comprehensive dataset from 53 wells has been utilized to meticulously develop and fine-tune various machine learning algorithms. These include Gradient Boosting, Linear Regression, Stochastic Gradient Descent, AdaBoost, Decision Trees, Random Forest, K-Nearest Neighbor, neural network, and Support Vector Machines. The results of the most effective machine learning models, specifically Gradient Boosting, Random Forest, AdaBoost, neural network (L-BFGS), and neural network (Adam), reveal exceptionally low values of mean absolute percent error (MAPE), root mean square error (RMSE), and mean squared error (MSE) in comparison to actual measurements of entry hole diameter. The recorded MAPE values are 4.6%, 4.4%, 4.7%, 4.9%, and 6.3%, with corresponding RMSE values of 0.057, 0.057, 0.058, 0.065, and 0.089, and MSE values of 0.003, 0.003, 0.003, 0.004, and 0.008, respectively. These low MAPE, RMSE, and MSE values verify the remarkably high accuracy of the generated models. This paper offers novel insights by demonstrating the improvements achieved in ongoing perforation operations through the application of a machine learning model for predicting entry hole diameter. The utilization of machine learning models presents a more accurate, expedient, real-time, and economically viable alternative to empirical models and deployed downhole cameras. Additionally, these machine learning models excel in accommodating a broad spectrum of guns, well completions, and reservoir parameters, a challenge that a singular empirical model struggled to address. Full article

In order to fully explore the development degree and distribution law of the structural plane of a tunnel surrounding rock in three-dimensional space, this paper studies the geometric characteristic parameters of a structural plane in the study area through field investigation, data acquisition and statistical analysis. The structural plane is divided into three dominant groups by using DIPS. v5. 103 software. The probability distribution model of occurrence, trace length, diameter and spacing of the structural plane is established. This paper focuses on the error correction of structural plane occurrence and the estimation of average trace length based on the rectangular window method. The discrete fracture network model is generated by using MATLAB R2021b software, and the discrete fracture network model is verified from three aspects: structural plane occurrence, average trace length and area density. The verification results are compared with the measured data, and the simulation results are in line with the actual situation on site. Based on the discrete fracture network model, the volume joint number of rock mass is calculated. Based on the JSR index, BQ classification method and RQD classification, the development degree of fractures and surrounding rock classification in this area are evaluated. A method of surrounding rock classification based on three evaluation indexes is discussed to comprehensively and accurately classify the quality of rock mass in this area. Full article

As a geological barrier for high-level radioactive waste (HLW) disposal in China, granite is crucial for blocking nuclide migration into the biosphere. However, the high uncertainty associated with the 3D geological system, such as the stochastic discrete fracture networks in granite, significantly impedes practical safety assessments of HLW disposal. This study proposes a Monte Carlo simulation (MCS)-based simulation framework for evaluating the long-term barrier performance of nuclide migration in fractured rocks. Statistical data on fracture geometric parameters, on-site hydrogeological conditions, and relevant migration parameters are obtained from a research site in Northwestern China. The simulation models consider the migration of three key nuclides, Cs-135, Se-79, and Zr-93, in fractured granite, with mechanisms including adsorption, advection, diffusion, dispersion, and decay considered as factors. Subsequently, sixty MCS realizations are performed to conduct a sensitivity analysis using the open-source software OpenGeoSys-5 (OGS-5). The results reveal the maximum and minimum values of the nuclide breakthrough time T_t (12,000 and 3600 years, respectively) and the maximum and minimum values of the nuclide breakthrough concentration C_max (4.26 × 10⁻⁴ mSv/a and 2.64 × 10⁻⁵ mSv/a, respectively). These significant differences underscore the significant effect of the uncertainty in the discrete fracture network model on long-term barrier performance. After the failure of the waste tank (1000 years), nuclides are estimated to reach the outlet boundary 6480 years later. The individual effective dose in the biosphere initially increases and then decreases, reaching a peak value of C_max = 4.26 × 10⁻⁴ mSv/a around 350,000 years, which is below the critical dose of 0.01 mSv/a. These sensitivity analysis results concerning nuclide migration in discrete fractured granite can enhance the simulation and prediction accuracy for risk evaluation of HLW disposal. Full article

Inverse modeling can estimate uncertain parameters in subsurface reservoirs and provide reliable numerical models for reservoir development and management. The traditional simulation-based inversion method usually requires numerous numerical simulations, which is time-consuming. Recently, deep learning-based surrogate models have been widely studied as an alternative to numerical simulation, which can significantly improve the solving efficiency of inversion. However, for reservoirs with complex fracture distribution, constructing the surrogate model of numerical simulation presents a significant challenge. In this work, we present a deep graph learning-based surrogate model for inverse modeling of fractured reservoirs. Specifically, the proposed surrogate model integrates the graph attention mechanisms to extract features of fracture network in reservoirs. The graph learning can retain the discrete characteristics and structural information of the fracture network. The extracted features are subsequently integrated with a multi-layer recurrent neural network model to predict the production dynamics of wells. A surrogate-based inverse modeling workflow is then developed by combining the surrogate model with the differential evolutionary algorithm. Numerical studies performed on a synthetic naturally fractured reservoir model with multi-scale fractures illustrate the performance of the proposed methods. The results demonstrate that the proposed surrogate model exhibits promising generalization performance of production prediction. Compared with tens of thousands of numerical simulations required by the simulation-based inverse modeling method, the proposed surrogate-based method only requires 1000 to 1500 numerical simulations, and the solution efficiency can be improved by ten times. Full article

Whether from the near-surface or at great depths, geothermal energy aims to harness the heat of the Earth to produce energy. Herein, emphasis is put on geothermal reservoirs and their cap rock in crystalline rocks, in particular, the basements of sedimentary basins and volcanic islands in the context of subduction. This study is based on a case study of three examples from around the world. The aim of this paper is to show how the study of newly formed minerals can help the exploration of geothermal reservoirs. The key parameters to define are the temperature (maximum temperature reached formerly), fluid pathways, and the duration of geothermal events. To define these parameters, numerous methods are used, including optical and electronic microscopy, X-ray diffraction, microthermometry on fluid inclusions, chlorite geothermometry, and geochemistry analysis, including that of isotopes. The key minerals that are studied herein are phyllosilicates and, in particular, clay minerals, quartz, and carbonates. They are formed because of hydrothermal alterations in fracture networks. These minerals can have temperatures of up to 300 °C (and they can cool down to 50 °C), and sometimes, they allow for one to estimate the cooling rate (e.g., 150 °C/200 ka). The duration of a hydrothermal event (e.g., at least 63 Ma or 650 ka, depending on the site) can also be established based on phyllosilicates. Full article

While geodetic measurements have long been used to assess landslides, seismic methods are increasingly recognized as valuable tools for providing additional insights into subsurface structures and mechanisms. This work aims to characterize the subsurface structures of the deep-seated gravitational slope deformation (DSGSD) at Heinzenberg through the integration of active and passive seismic measurements. Seismic techniques can hereby deliver additional information on the subsurface structure and mechanisms involved, e.g., the degree of rock mass degradation, the resonant frequencies of the potentially unstable compartments, and the local fracture network orientations that are influenced by wavefield polarization. By employing advanced methods such as H/V analysis, site-to-reference spectral ratios, polarization analysis, surface wave analysis, and the joint multizonal transdimensional Bayesian inversion of velocity structures, we establish a comprehensive baseline model of the landslide at five selected sites. This baseline model shall help identify potential changes after the refilling of Lake Lüsch, which started in 2021. Our results reveal the rupture surface of the DSGSD at various depths ranging from 30 m at the top to over 90 m in the middle of the slope. Additionally, we estimate key parameters including the shear wave velocities of the different rock masses. The 2D geophysical profiles and rock mass properties contribute to the understanding of the subsurface geometry, geomechanical properties, and potential water pathways. This study demonstrates the significance of integrating seismic methods with traditional geodetic measurements and geomorphologic analysis techniques for a comprehensive assessment of landslides, enhancing our ability to monitor and mitigate hazardous events. Full article

When drilling deep wells, it is important to regulate the formation pressure and prevent kicks. This is achieved by controlling the equivalent circulation density (ECD), which becomes crucial in high-pressure and high-temperature wells. ECD is particularly important in formations where the pore pressure and fracture pressure are close to each other (narrow windows). However, the current methods for measuring ECD using downhole sensors can be expensive and limited by operational constraints such as high pressure and temperature. Therefore, to overcome this challenge, two novel models named ECD_effc.m and MW_effc.m were developed to predict ECD and mud weight (MW) from surface-drilling parameters, including standpipe pressure, rate of penetration, drill string rotation, and mud properties. In addition, by utilizing an artificial neural network (ANN) and a support vector machine (SVM), ECD was estimated with a correlation coefficient of 0.9947 and an average absolute percentage error of 0.23%. Meanwhile, a decision tree (DT) was employed to estimate MW with a correlation coefficient of 0.9353 and an average absolute percentage error of 1.66%. The two novel models were compared with artificial intelligence (AI) techniques to evaluate the developed models. The results proved that the two novel models were more accurate with the value obtained from pressure-while-drilling (PWD) tools. These models can be utilized during well design and while drilling operations are in progress to evaluate and monitor the appropriate mud weight and equivalent circulation density to save time and money, by eliminating the need for expensive downhole equipment and commercial software. Full article

The gas flow in shale reservoirs is controlled by gas desorption diffusion and multiple flow mechanisms in the shale matrix. The treatment of hydraulic fracturing injects a large amount of fracturing fluids into shale reservoirs, and the fracturing fluids can only be recovered by 30~70%. The remaining fracturing fluid invades the reservoir in the form of a water invasion layer. In this paper, by introducing the concept of a water invasion layer, the hydraulic fracture network is di-vided into three zones: major fracture, water invasion layer and stimulated reservoir volume (SRV). The mathematical model considering gas desorption, the water invasion layer and gas–water two-phase flow in a major fracture is established in the Laplace domain, and the semi-analytical solution method is developed. The new model is validated by a commercial simulator. A field case from WY shale gas reservoir in southwestern China is used to verify the utility of the model. Several key parameters of major fracture and SRV are interpreted. The gas–water two-phase flow model established in this paper provides theoretical guidance for fracturing effectiveness evaluation and an efficient development strategy of shale gas reservoirs. Full article

The development of tight gas reservoirs presents a significant challenge for sustainable development, as it requires specialized techniques that can have adverse environmental and social impacts. To address these challenges, efficient development technologies, such as multistage hydraulic fracturing, have been adopted to enable access to previously inaccessible natural gas resources, increase energy efficiency and security, and minimizing environmental impacts. This paper proposes a novel evaluation method to analyze the post fracturing productivity controlled by complex fault fracture zones in tight reservoirs. In this article, a systematic method to evaluate stimulated reservoir volume (SRV) and fault-fracture zone complexity after stimulation was established, along with the analysis and prediction of productivity through coupled fall-off and well-test analyses. Focusing on the Xujiahe formation in the Tongnanba anticline of northeastern Sichuan Basin, a 3D geological model was developed to analyze planar heterogeneity. The fall-off analytical model, coupled with rock mechanical parameters and fracturing parameters such as injection rates, fracturing fluid viscosity, and the number of clusters within a single stage, was established to investigate the fracture geometric parameters and complexities of each stage. The trilinear flow model was used to solve the well-test analysis model of multi-stage fractured horizontal wells in tight sandstone gas reservoirs, and well-test curves of the heterogeneous tight sandstone gas fracture network model were obtained. The results show that hydraulic fractures connect the natural fractures in fault-fracture zones. An analysis of the relationship between the fracture geometric outcomes of each segment and the net pressure reveals that as the net pressure in the fracture increases, the area ratio of natural fractures to main fractures increases notably, whereas the half length of the main fracture exhibits a decreasing trend. The overall area of natural fractures following stimulation is 7.64 times greater than that of the main fractures and is mainly a result of the extensive development of natural fractures in the target interval. As the opening ratio of natural fractures increases, the length of the main fractures decreases accordingly. Therefore, increasing net pressure within fractures will significantly enhance the complexity of fracturing fractures in shale gas reservoirs. Furthermore, the initial production of Well X1–10, which is largely controlled by fault-fracture zones, and the cumulative gas production after one year, are estimated. The systematic evaluation method in this study proposed a new way to accurately measure fracturing in tight reservoirs, which is a critical and helpful component of sustainable development in the natural gas industry. Full article

Volumetric fracture intensity (P₃₂) is a parameter that plays a major role in the mechanical and hydraulic behaviour of rock masses. While methods such as Ground Penetrating Radar (GPR) are available to map the 3D geometrical characteristics of the fractures, the direct measurement of P₃₂ at a resolution compatible with geotechnical applications is not yet possible. As a result, P₃₂ can be estimated from the borehole and surface data using either simulation or analytical solutions. In this paper, we use Discrete Fracture Network (DFN) models to address the problem of estimating P₃₂ using information from boreholes (1D data). When calculating P₃₂ based on Terzaghi Weighting, it is common practice to use drill run lengths and limit the minimum angle between the borehole and the intersected fractures. The analysis presented in this paper indicated that limiting the minimum angle of intersection would result in an underestimation of the calculated P₃₂. Additionally, the size of the interval has a significant impact on the variability of the calculated P₃₂. We propose a methodology to calculate P₃₂ using variable lengths, depending on the angle between the fractures and the borehole. This methodology allows the capture of the spatial variation in intensity and simultaneously avoids artificially increasing or decreasing the intensity sampled along borehole intervals. Additionally, this work has addressed the impact of boundary effects in DFN models and proposes a methodology to mitigate them. Full article

This paper focuses on the compressive strength of Glass fiber reinforced polymer (GFRP)-confined reinforced concrete columns. Data from 114 sets of GFRP-confined reinforced concrete columns were collected to evaluate the researchers’ and proposed model. A data-driven machine learning model was used to model the compressive strength of the GFRP-confined reinforced concrete columns and investigate the importance and sensitivity of the parameters affecting the compressive strength. The results show that the researchers’ model facilitates the study of the compressive strength of confined columns but suffers from a large coefficient of variation and too high or conservative estimation of compressive strength. The back propagation (BP) neural network has the best accuracy and robustness in predicting the compressive strength of the confined columns, with the coefficient of variation of only 14.22%, and the goodness of fit for both the training and testing sets above 0.9. The parameters that have an enormous influence on compressive strength are the concrete strength and FRP thickness, and all the parameters, except the fracture strain of FRP, are positively or inversely related to the compressive strength. Full article

Gas reservoir numerical simulation is an important method to optimize the development strategy of shale gas reservoirs which has been influenced by the multi-stage fracture. The regular fracture network model was used to build a conventional numerical simulation, in which it was difficult to show the true situation of fracture propagation. However, the physical parameters not only affect the production, but also influence the stimulation effect; moreover, the quality of the fracturing effect also affects the production which causes the input and out parameters to be inaccurate. To solve this problem, the process simulation must be completed from geology to engineering to gas reservoir. The main controlling factors of production are identified with geological and engineering factors such as horizontal stage length, the volume of fracturing fluid, well spacing, production allocation, and proppant mass. Therefore, on the basis of the integrated simulation method of a hydraulic fracturing network simulation and an unstructured grid high-precision numerical simulation, this paper builds an integrated numerical simulation of a shale gas reservoir coupled with geology and engineering to optimize the development strategy with production as the target. Taking four wells of a platform as an example, the EUR (estimated ultimate recovery) has increased by 25% after the optimization of the development strategy. Full article